Management system structure vs. Behavior - a supply chain simulation analysis

• Autorzy: Pietroń Roman

Identyfikatory

DOI

10.29119/1641-3466.2023.169.35

• Języki publikacji: EN

Abstrakty

EN

Purpose: The purpose of this article is to present a research report on a system dynamics simulation modeling and experimenting of bullwhip effect (BWE) to examine effectiveness of some selected inventory control policies with down- and upstream information flow in a Beer Distribution Game (BDG) of a supply chain structure. Design/methodology/approach: The impact of systems’ structures and decision making policies in supply chains or logistics systems are measured and analyzed by an application of systems thinking paradigms and approaches. Particularly, the continuous simulation modeling approach with systems thinking Iceberg model metaphor, allowing to focus on strategic aspects of management with some recommendation to design better structures and decision making policies are taken. For the bullwhip effect analysis of a supply chain example (based on BDG model), a System Dynamics (SD) continuous simulation modeling method with some proposals in order to analyze feedback loop dominance are undertaken to explain supply chain behaviors and to make some sensitivity analysis for decision making (inventory control) policies. Findings: The research findings outline the impact of cause - effect relations, feedback loops polarities, and decision making policies to particular behaviors of the BDG supply chain. Research limitations/implications: Because of complexity of heuristic methods for feedback loop dominance analysis only simple approach was applied (LPD), and some selected scenario for simulation experiments were undertaken resulting in limited conclusions. Practical implications: The conclusions of the research draw some practical recommendations for a design of information sharing system and an effectiveness of some inventory control policies to be applied in supply chains. Social implications: One of the systems thinking elements in practical management is an influence to mental models of managers and decision makers. Managers in supply chain systems particularly need some recommendations to avoid bullwhip effect negative impacts. Additionally, managers and also scholars still call for more research to investigate the design and decision making in supply chains, therefore systems thinking simulation research can bridge the gap between traditional operations research and management with other approaches to provide insight into supply-chain dynamics and deliver impactful suggestions to managers. Originality/value: The paper gives a concept of supply chain dynamic analysis by an application of Iceberg model systems thinking metaphor, feedback loop dominance analysis, and a measurement of some selected inventory control policies effectiveness.

Słowa kluczowe

EN

supply chain; bullwhip effect; inventory control; simulation modeling

PL

łańcuch dostaw; efekt byczego bicza; kontrola zapasów; modelowanie symulacyjne

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
• Rocznik: 2023
• Tom: z. 169
• Strony: 575--597
• Opis fizyczny: Bibliogr. 41 poz.

Twórcy

autor

Pietroń Roman

• Wrocław University of Science and Technology

• https://orcid.org/0000-0002-8191-8889

Bibliografia

• 1. Abdelbari, H., Shafi, K. (2017). A computational Intelligence-based Method to ‘Learn’ Causal Loop Diagram-like Structures from Observed Data. System Dynamics Review, Vol. 33, No. 1, pp. 3-33.
• 2. Akkermans, H., Dellaert, N. (2005). The rediscovery of industrial dynamics. The contribution of system dynamics to supply chain management in a dynamic and fragmented world, System Dynamics Review, Vol. 21, No. 3, pp. 173-186.
• 3. Bhattacharya, R., Bandyopadhyay, S. (2011). A review of the causes of bullwhip effect in a supply chain. International Journal of Advanced Manufacturing Technology, Vol. 54, pp. 1245-1261.
• 4. Bolton, G.E., Katok, E. (2008). Learning by doing in the newsvendor problem: A laboratory investigation of the role of experience and feedback. Manufacturing & Service Operations Management, Vol. 10, No. 3, pp. 519-538.
• 5. Croson, R., Donohue, K. (2005). Upstream versus downstream information and its impact on the bullwhip effect. System Dynamics Review, Vol. 21, No. 3, pp. 249-260.
• 6. Dass, M., Fox, G.L. (2011). A holistic network model for supply chain analysis. International Journal ofProduction Economics, Vol. 131, No. 2, pp. 587-594.
• 7. Ding, H., Guo, B., Liu, Z. (2011). Information sharing and profit allotment based on supply chain cooperation. International Journal ofProduction Economics, Vol. 133, pp. 70-79.
• 8. Dobos, I. (2011). The analysis of bullwhip effect in a HMMS-type supply chain. International Journal of Production Economics, Vol 131, No. 1, pp. 250-256.
• 9. Duc, T. T., Luong, H.T., Kim, Y.-D. (2008). A measure of bullwhip effect in supply chains with a mixed autoregressive - moving average demand process. European Journal of Operational Research, Vol 187, pp. 243-256.
• 10. Duc, T.T.H., Luong, H.T., Kim, Y.-D. (2010). Effect of the third-party warehouse on bullwhip effect and inventory cost in supply chains. International Journal of Production Economics, Vol. 124, No. 2, pp. 395-407.
• 11. Ford, D.N. (1999). A behavioral approach to feedback loop dominance analysis. System Dynamics Review, Vol. 15, No. 1, pp. 3-36.
• 12. Forrester, J.W. (1961). Industrial Dynamics. New York-London: MIT Press, John Wiley & Sons, Ltd.
• 13. Forrester, J.W. (1972). Principles of Systems. Cambridge Massachusetts: Wright-Allen Press.
• 14. Gonęalves, P., Moshtari, M.H. (2021). The impact of information visibility on ordering dynamics in a supply chain: a behavioral perspective. System Dynamics Review, Vol. 37, No. 2-3, pp. 126-154.
• 15. Güneralp, B. (2006). Towards coherent loop dominance analysis: progress in eigenvalue elasticity analysis. System Dynamics Review, Vol. 22, No. 3, pp. 263-289.
• 16. Hayward, J., Boswell, G.P. (2014). Model behavior and the concept of loop impact: A practical method. System Dynamics Review, Vol. 30, No. 1-2, pp. 29-57.
• 17. Huang, J., Howley, E., Duggan, J. (2012). Observations on the shortest independent loop set algorithm. System Dynamics Review, Vol. 28, No. 3, pp. 276-280.
• 18. Jakšič, M., Rusjan, B. (2008). The effect of replenishment policies on the bullwhip effect: A transfer function approach. European Journal of Operational Research, Vol. 184, No. 3, pp. 946-961.
• 19. Kampmann, Chr., E. (2012), Feedback loop gains and system behavior. System Dynamics Review, Vol. 28, No. 4, pp. 370-395.
• 20. Kampmann, Chr., E., Oliva, R. (2006). Loop eigenvalue elasticity analysis: three case studies. System Dynamics Review, Vol. 22, No. 2, pp. 141-162.
• 21. Kampmann, Chr., E., Oliva, R. (2008). Structural dominance analysis and theory building in system dynamics. Systems Research and Behavioral Science, Vol. 25, No. 4, pp. 505¬519.
• 22. Kampmann, Chr., E., Sterman, J.D. (2014). Do markets mitigate misperceptions of feedback. System Dynamics Review, Vol. 30, No. 3, pp. 123-160.
• 23. Kristianto, Y., Helo, P., Jiao, J., Sandhu, M. (2012). Adaptive fuzzy vendor managed inventory control for mitigating the bullwhip effect in supply chains. European Journal of Operational Research, Vol. 216, No, 2, pp. 346-355.
• 24. Liang, W.-Y., Huang, Ch.-Ch. (2006). Agent-based demand forecast in multi-echelon supply chain. Decision Support Systems, Vol. 42, No. 1, pp. 390-407.
• 25. Machuca, J.A.D., Pozo Barajas, R. (1997). A computerized network version of the Beer Game via the Internet. System Dynamics Review, Vol. 13, No. 4, pp. 323-342.
• 26. Mesjasz-Lech, A. (2012). Efekty byczego bicza a zarzadzanie zapasami w łańcuchu dostaw. Logistyka, Nr 5, pp. 134-141.
• 27. Mojtahedzadeh, M. (2011). Consistency in explaining model behavior based on its feedback structure. System Dynamics Review, Vol. 27, No. 4, pp. 358-373.
• 28. Mojtahedzadeh, M., Andersen, D., Richardson, G.P. (2004). Using Digest to implement the pathway participation method for detecting influential system structure. System Dynamics Review, Vol. 20, No. 1, pp. 1-20.
• 29. Narayanan, A., Moritz, B. (2015). Decision Making and Cognition in Multi-Echelon Supply Chains: An Experimental Study. Production and Operations Management, Vol. 24, No. 8, pp. 1216-1234.
• 30. Naumov, S., Oliva, R. (2018). Refinements on eigenvalue elasticity analysis: interpretation of parameter elasticities. System Dynamics Review, Vol. 34, No. 3, pp. 426-437.
• 31. Oliva, R. (2004). Model structure analysis through graph theory: partition heuristics and feedback structure decomposition. System Dynamics Review, Vol. 20, No. 4, pp. 313-336.
• 32. Ouyang, Y., Li, X. (2010). The bullwhip effect in supply chain networks. European Journal of Operational Research, Vol. 201, No. 3, pp. 799-810.
• 33. Rahmandad, H., Repenning, N., Sterman, J. (2009). Effects of feedback delay on learning. System Dynamics Review, Vol. 25, No. 4, pp. 309-338.
• 34. Richardson, G.P. (1995). Loop polarity, loop dominance, and the concept of dominant polarity. System Dynamics Review, Vol. 11, No. 1, pp. 67-88.
• 35. Schoenenberger, L., Schmid, A., Schwaninger, M. (2015). Towards the algorithmic detection of archetypal structures in system dynamics. System Dynamics Review, Vol. 31, No. 1-2, pp. 66-85.
• 36. Sodhi, M.M., Tang, S., Christopher, S. (2011). The incremental bullwhip effect of operational deviations in an arborescent supply chain with requirements planning. European Journal of Operational Research, Vol. 215, No. 2, pp. 374-382.
• 37. Sterman, J.D. (1989). Modeling Managerial Behavior: Misperceptions of Feedback in a Dynamic Decision Making Experiment. Management Science, Vol. 35, No. 3, pp. 321-339.
• 38. Sterman, J.D., Dogan, G. (2015). Behavioral causes of phantom ordering in supply chains. Journal of Operations Management, Vol. 39-40, pp. 6-22.
• 39. Wąsik, B. (1992). Analiza systemu produkcji i dystrybucji przy użyciu planszowej gry symulacyjnej "Beer Distribution Game". In: E. Radosiński (ed.), Modelowanie symulacyjne i sztuczna inteligencja w analizie przedsiębiorstwa. Monografie PTS, nr 1 (pp. 60-63). Wrocław.
• 40. Yucel, G., Barlas, Y. (2011). Automated parameter specification in dynamic feedback models based on behavior pattern features. System Dynamics Review, Vol. 27, No. 2, pp. 195-215.
• 41. Zhang, X., Burke, G.J. (2011). Analysis of compound bullwhip effect causes. European Journal of Operational Research, Vol. 210, No. 3, pp. 514-526.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).